Data Fellowship Programme Overview
This programme is designed for aspiring and new data analysts, empowering teams with programming, data modelling, and analysis skills to effectively utilise business data. It is part of our Data Academy Programmes.
Key Skills Gained:
SQL
Data visualisation
Advanced BI
Machine learning
Business Outcomes:
Boost productivity: Increase access to clean, structured data that can be utilised by the business.
Improve business decision-making: Help teams leverage data in a visual context and communicate actionable insights.
Democratise data skills: Reduce reliance on central data teams by equipping business functions with data analysis skills.
Reduce costs: Lower the number of manual data tasks that are often at risk of human error.
Apprenticeship Qualification Achieved: Level 4 Data Analyst
Duration: 13 month delivery, plus 2 month assessment
Software Requirements: Data Fellowship Software Requirements
Data Fellowship Indicative Curriculum Breakdown
Data analysis essentials
Month 1: Foundations of a Data Analyst
Learn the basics of data analysis and key statistical concepts, and gain familiarity with various data types and structures.
Develop an understanding and skills in exploratory data analysis to filter, sort and calculate descriptive statistics.
Month 2: Foundations of data management
Understand principles of data management and governance, developing skills in ensuring data accuracy and quality.
Maintain data integrity and compliance with regulations by developing and applying robust data governance frameworks.
Visualising and integrating data with a BI tool
Month 3: Visualising data for stakeholders
Design impactful data visualisations and present insights effectively. Tailor data stories to different audiences and improve the clarity of data for informed decision-making.
Develop communication skills and enhance collaboration with stakeholders.
Month 4: Integrating data for business impact
Understand database design principles and data modeling.
Learn techniques for expanding and integrating datasets, building efficient and scalable database systems with data accessibility and usability at its core.
Month 5: Data analysis hackathon and EPA prep session
Working together on a challenge that allows apprentices to use the skills learned so far.
Working session to learn more about the end point assessment (EPA), practice for interviews, and work on evidence.
Levelling up data analysis with statistics and AI
Month 6: Data integration and analysis techniques
Develop skills in SQL and data integration techniques, combining and manipulating data from various sources.
Enable seamless data integration across platforms and support greater granularity in strategic decision-making with comprehensive datasets.
Month 7: Advanced analytics and statistical methods
Understand and apply advanced statistical techniques, deriving deeper insights from data.
Uncover hidden trends and patterns for precise decision-making and evaluate data for use in predictive analytics.
Month 8: Statistics hackathon and EPA prep session
Working together on a challenge that allows apprentices to use the skills learned so far.
Working session to learn more about the end point assessment (EPA), practice for interviews, and work on evidence.
Machine learning and predictive analytics
Month 9: Predicting the future with time series forecasting
Analyse time series data and build forecasting models.
Evaluate and improve forecasting accuracy for strategic planning and anticipate trends and patterns for more effective forecasts and planning.
Month 10: Introduction to machine learning
Learn the basics of machine learning and model implementation and how to develop, train and optimise models.
Incorporate machine learning for more effective automation and drive innovation with predictive analytics.
Month 11: Machine learning hackathon
Working together on a challenge that allows apprentices to use the skills learned so far.
Month 12: End point assessment (EPA) preparation
Working session to learn more about the EPA, practice for interviews, and work on evidence.
Note: This is an example curriculum, and specific details may vary per cohort.
Data Fellowship Indicative Delivery Model
Monthly delivery model, approx. 27 hours per month total commitment. The exact time commitment will be outlined in the training plan that apprentices will receive at the start of their apprenticeship.
Structured Learning (~45% - 12 hours/month):
Asynchronous learning (6 hours): Online, self-paced content that sets the foundation of skills for the module.
Group learning (5 hours): Live, instructor-led, small-group interactive learning that dives deeper and reinforces the asynchronous content.
Coach and peer support (1 hour): Coach and peer support.
Working in Existing Role (~55% - 15 hours/month):
Work-based tasks (7 hours): Structured tasks to provide the opportunity to apply learnings in real work context.
Independent applied learning (8 hours): Application of learning to apprentices’ existing day to day activities.